Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/devamoghs/machine-learning-with-python
Small scale machine learning projects to understand the core concepts . Give a Star 🌟If it helps you. BONUS: Interview Bank coming up..!
https://github.com/devamoghs/machine-learning-with-python
beginner-friendly data-science deep-learning exercises machine-learning practice-project python python-3 scikit-learn
Last synced: 1 day ago
JSON representation
Small scale machine learning projects to understand the core concepts . Give a Star 🌟If it helps you. BONUS: Interview Bank coming up..!
- Host: GitHub
- URL: https://github.com/devamoghs/machine-learning-with-python
- Owner: devAmoghS
- License: mit
- Created: 2018-03-02T14:54:11.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-08-04T13:01:19.000Z (5 months ago)
- Last Synced: 2025-01-02T21:08:21.741Z (7 days ago)
- Topics: beginner-friendly, data-science, deep-learning, exercises, machine-learning, practice-project, python, python-3, scikit-learn
- Language: Python
- Homepage:
- Size: 840 KB
- Stars: 1,186
- Watchers: 45
- Forks: 182
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
# Machine-Learning-with-Python  
## Star History
[](https://star-history.com/#devAmoghS/Machine-Learning-with-Python&Date)
## Small scale machine learning projects to understand the core concepts (order: oldest to newest)
* Topic Modelling using **Latent Dirichlet Allocation** with newsgroups20 dataset, implemented with Python and Scikit-Learn
* Implemented a simple **neural network** built with Keras on MNIST dataset
* Stock Price Forecasting on Google using **Linear Regression**
* Implemented a simple a **social network** to learn basics of Python
* Implemented **Naives Bayes Classifier** to filter spam messages on SpamAssasin Public Corpus
* **Churn Prediction Model** for banking dataset using Keras and Scikit-Learn
* Implemented **Random Forest** from scratch and built a classifier on Sonar dataset from UCI repository
* Simple Linear Regression in Python on sample dataset
* **Multiple Regression** in Python on sample dataset
* **PCA and scaling** sample stock data in Python [working_with_data]
* **Decision Trees** in Python on sample dataset
* **Logistic Regression** in Python on sample dataset
* Built a neural network in Python to defeat a captcha system
* Helper methods include commom operations used in **Statistics, Probability, Linear Algebra and Data Analysis**
* **K-means clustering** with example data; **clustering colors** with k-means; **Bottom-up Hierarchical Clustering**
* Generating Word Clouds
* Sentence generation using n-grams
* Sentence generation using **Grammars and Automata Theory; Gibbs Sampling**
* Topic Modelling using Latent Dirichlet Analysis (LDA)
* Wrapper for using Scikit-Learn's **GridSearchCV** for a **Keras Neural Network**
* **Recommender system** using **cosine similarity**, recommending new interests to users as well as matching users as per common interests
* Implementing different methods for **network analysis** such as **PageRank, Betweeness Centrality, Closeness Centrality, EigenVector Centrality**
* Implementing methods used for **Hypothesis Inference** such as **P-hacking, A/B Testing, Bayesian Inference**
* Implemented **K-nearest neigbors** for next presedential election and prediciting voting behavior based on nearest neigbors.
## Installation notes
MLwP is built using Python 3.5. The easiest way to set up a compatible
environment is to use [Conda](https://conda.io/). This will set up a virtual
environment with the exact version of Python used for development along with all the
dependencies needed to run MLwP.
1. [Download and install Conda](https://conda.io/docs/download.html).
2. Create a Conda environment with Python 3.
(**Note**: enter ```cd ~``` to go on **$HOME** , then perform these commands)
```
conda create --name *your env name* python=3.5
```
You will get the following, mlwp-test is the env name used in this example
```
Solving environment: done
## Package Plan ##
environment location: /home/user/anaconda3/envs/mlwp-test
added / updated specs:
- python=3.5
The following NEW packages will be INSTALLED:
ca-certificates: 2018.12.5-0
certifi: 2018.8.24-py35_1
libedit: 3.1.20181209-hc058e9b_0
libffi: 3.2.1-hd88cf55_4
libgcc-ng: 8.2.0-hdf63c60_1
libstdcxx-ng: 8.2.0-hdf63c60_1
ncurses: 6.1-he6710b0_1
openssl: 1.0.2p-h14c3975_0
pip: 10.0.1-py35_0
python: 3.5.6-hc3d631a_0
readline: 7.0-h7b6447c_5
setuptools: 40.2.0-py35_0
sqlite: 3.26.0-h7b6447c_0
tk: 8.6.8-hbc83047_0
wheel: 0.31.1-py35_0
xz: 5.2.4-h14c3975_4
zlib: 1.2.11-h7b6447c_3
Proceed ([y]/n)? *Press y*
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use:
# > source activate mlwp-test
#
# To deactivate an active environment, use:
# > source deactivate
#
```
The environment is successfully created.
3. Now activate the Conda environment.
```
source activate *your env name*
```
You will get the following
```
(mlwp-test) amogh@hp15X34:~$
```
Enter `conda list` to get the list of available packages
```
(mlwp-test) amogh@hp15X34:~$ conda list
# packages in environment at /home/amogh/anaconda3/envs/mlwp-test:
#
# Name Version Build Channel
ca-certificates 2018.12.5 0
certifi 2018.8.24 py35_1
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libgcc-ng 8.2.0 hdf63c60_1
libstdcxx-ng 8.2.0 hdf63c60_1
ncurses 6.1 he6710b0_1
openssl 1.0.2p h14c3975_0
pip 10.0.1 py35_0
python 3.5.6 hc3d631a_0
readline 7.0 h7b6447c_5
setuptools 40.2.0 py35_0
sqlite 3.26.0 h7b6447c_0
tk 8.6.8 hbc83047_0
wheel 0.31.1 py35_0
xz 5.2.4 h14c3975_4
zlib 1.2.11 h7b6447c_3
```
4. Install the required dependencies.
```
(mlwp-test) amogh@hp15X34:~$ conda install --yes --file *path to requirements.txt*
```
5. In case you are not able to install the packages or getting `PackagesNotFoundError`
Use the following command ` conda install -c conda-forge *list of packages separated by space*`. For more info, refer issue [#3](https://github.com/devAmoghS/Machine-Learning-with-Python/issues/3) **Unable to install requirements**
## How good is the code ?
* It is well tested
* It passes style checks (PEP8 compliant)
* It can compile in its current state (and there are relatively no issues)
## How much support is available?
* FAQs (coming soon)
* Documentation (coming soon)
## Issues
Feel free to submit issues and enhancement requests.
## Contributing
Please refer to each project's style guidelines and guidelines for submitting patches and additions. In general, we follow the "fork-and-pull" Git workflow.
1. **Fork** the repo on GitHub
2. **Clone** the project to your own machine
3. **Commit** changes to your own branch
4. **Push** your work back up to your fork
5. Submit a **Pull request** so that we can review your changes
NOTE: Be sure to merge the latest from "upstream" before making a pull request!